Progression of Patterns (POP): A Machine Classifier Algorithm to Identify Glaucoma Progression in Visual Fields

Goldbaum, Michael H.; Lee, Intae; Jang, Gil-Jin; Balasubramanian, Madhusudhanan; Sample, Pamela A.; Weinreb, Robert N.; Liebmann, Jeffrey M.; Girkin, Christopher A.; Anderson, Douglas R.; Zangwill, Linda M.; Fredette, Marie Josée; Jung, Tzyy Ping; Medeiros, Felipe A.; Bowd, Christopher

doi:10.1167/iovs.11-8363

Cited by 45 publications

(44 citation statements)

References 19 publications

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“…We also compared the GEM-POP outcome to the recently developed VIM progression of patterns (VIM-POP) method [19], for the same eyes and for the same follow-up duration, and found that GEM-POP performed slightly but not significantly better than VIM-POP (sensitivity for VIM-POP was 26.6% compared to 28.9% for GEM-POP).…”

Section: Resultsmentioning

confidence: 99%

“…The VFI and MD are global indices provided for each individual test. We also compared the performance of GEM with that of the previously described VIM-based method [19]. We will provide the details of the assessments in the subsequent sections.…”

Section: Glaucoma Progression Detection Pipelinementioning

confidence: 99%

“…Revealing these patterns can serve as a fundamental step toward more specific mining and learning tasks [18]. Such learning tasks recently have been applied to the detection and monitoring of glaucoma [9], [19]–[21]. …”

Section: Introductionmentioning

confidence: 99%

“…Recent advances in unsupervised classification techniques provide an alternative approach for glaucoma-related progression detection from SAP. For instance, machine learning and data mining techniques have been used to recognize glaucoma-related SAP visual field defect patterns and detect progression of glaucoma-related visual field defects [9], [19], [20], [34]. …”

Section: Introductionmentioning

confidence: 99%

“…We hypothesize that change in GEM-defined patterns of defect would perform as well as or better at detecting known glaucomatous change than other techniques. If our hypothesis is confirmed, change in GEM-defined patterns might be a better candidate for glaucoma progression detection from SAP data than change in VIM-defined patterns, because computational requirements to identify patterns are significantly less using GEM than VIM, and tracking change in VIM-defined patterns (i.e., POP) already has been shown to outperform some commercially available progression algorithms [19]. …”

Section: Introductionmentioning

confidence: 99%

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Learning From Data: Recognizing Glaucomatous Defect Patterns and Detecting Progression From Visual Field Measurements

Yousefi

Goldbaum

Balasubramanian

et al. 2014

IEEE Trans. Biomed. Eng.

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A hierarchical approach to learn from visual field data was adopted to identify glaucomatous visual field defect patterns and to detect glaucomatous progression. The analysis pipeline included three stages, namely, clustering, glaucoma boundary limit detection, and glaucoma progression detection testing. First, cross-sectional visual field tests collected from each subject were clustered using a mixture of Gaussians and model parameters were estimated using expectation maximization. The visual field clusters were further estimated to recognize glaucomatous visual field defect patterns by decomposing each cluster into several axes. The glaucoma visual field defect patterns along each axis then were identified. To derive a definition of progression, the longitudinal visual fields of stable glaucoma eyes on the abnormal cluster axes were projected and the slope was approximated using linear regression (LR) to determine the confidence limit of each axis. For glaucoma progression detection, the longitudinal visual fields of each eye on the abnormal cluster axes were projected and the slope was approximated by LR. Progression was assigned if the progression rate was greater than the boundary limit of the stable eyes; otherwise, stability was assumed. The proposed method was compared to a recently developed progression detection method and to clinically available glaucoma progression detection software. The clinical accuracy of the proposed pipeline was as good as or better than the currently available methods.

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Section: Resultsmentioning

confidence: 99%

Section: Glaucoma Progression Detection Pipelinementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Learning From Data: Recognizing Glaucomatous Defect Patterns and Detecting Progression From Visual Field Measurements

Yousefi

Goldbaum

Balasubramanian

et al. 2014

IEEE Trans. Biomed. Eng.

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Evaluating New Ophthalmic Digital Devices for Safety and Effectiveness in the Context of Rapid Technological Development

et al. 2019

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s computation has upended communication, financial transactions,manufacturing,media,andtransportation,sotoohas it transformed ophthalmology with the introduction of innovationssuchascross-sectionalimaging,electronicmedicalrecords,and refractive surgery. The past decade has seen increasing miniaturization and the ascendency of mobile devices and distributed computing. In 2017,thepercentageofAmericanswithsmartphonesreached77%and the number of global internet users reached approximately 52% of the world's population. [1][2][3] With this increase in connectivity, patients are demanding improved access to their electronic health information and communication with health care practitioners. Such technology could help close gaps in health care access for rural and underserved areas. Artificial intelligence (AI) and machine learning techniques have experienced a renaissance as improved computational power has enabled the development of deep neural networks. 4,5 This improvement enables highly accurate image classification, natural language processing, and image segmentation. 6 IMPORTANCE The US Food and Drug Administration's medical device regulatory pathway was initially conceived with hardware devices in mind. The emerging market for ophthalmic digital devices necessitates an evolution of this paradigm.OBJECTIVES To facilitate innovation in ophthalmic digital health with attention to safety and effectiveness.EVIDENCE REVIEW This article presents a summary of the presentations, discussions, and literature review that occurred during a joint Ophthalmic Digital Health workshop of the American Academy of Ophthalmology, the American Academy of Pediatrics, the American Association for Pediatric Ophthalmology and Strabismus, the American Society of Cataract and Refractive Surgery, the American Society of Retina Specialists, the Byers Eye Institute at Stanford and the US Food and Drug Administration.FINDINGS Criterion standards and expert graders are critically important in the evaluation of automated systems and telemedicine platforms. Training at all levels is important for the safe and effective operation of digital health devices. The risks associated with automation are substantially increased in rapidly progressive diseases. Cybersecurity and patient privacy warrant meticulous attention. CONCLUSIONS AND RELEVANCEWith appropriate attention to safety and effectiveness, digital health technology could improve screening and treatment of ophthalmic diseases and improve access to care.

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The Role of Artificial Intelligence in the Diagnosis and Management of Glaucoma

Kapoor

Whigham

Al-Aswad

2019

Curr Ophthalmol Rep

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Progression of Patterns (POP): A Machine Classifier Algorithm to Identify Glaucoma Progression in Visual Fields

Abstract: POP, with its display of DOC of progression and its identification of progressing VF defect pattern, adds to the information available to the clinician for detecting VF progression.

Cited by 45 publications

References 19 publications

Learning From Data: Recognizing Glaucomatous Defect Patterns and Detecting Progression From Visual Field Measurements

Learning From Data: Recognizing Glaucomatous Defect Patterns and Detecting Progression From Visual Field Measurements

Evaluating New Ophthalmic Digital Devices for Safety and Effectiveness in the Context of Rapid Technological Development

The Role of Artificial Intelligence in the Diagnosis and Management of Glaucoma

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